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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains ... Mather, Brodie; Dorr, Bonnie J; Dalton, Adam. - : arXiv, 2022 BASE Show details 2 Detecting Asks in SE attacks: Impact of Linguistic and Structural Knowledge ... Dorr, Bonnie J.; Bhatia, Archna; Dalton, Adam. - : arXiv, 2020 BASE Show details 3 Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation ... Bhatia, Archna; Dalton, Adam; Mather, Brodie. - : arXiv, 2020 BASE Show details 4 Statistical modality tagging from rule-based annotations and crowdsourcing ... Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : arXiv, 2015 BASE Show details 5 Use of Modality and Negation in Semantically-Informed Syntactic MT ... Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J.. - : arXiv, 2015 BASE Show details 6 A Modality Lexicon and its use in Automatic Tagging ... Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J.. - : arXiv, 2014 BASE Show details 7 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ... Baker, Kathryn; Bloodgood, Michael; Callison-Burch, Chris. - : arXiv, 2014 BASE Show details 8 Computing Lexical Contrast ... Mohammad, Saif M.; Dorr, Bonnie J.; Hirst, Graeme; Turney, Peter D.. - : arXiv, 2013 Abstract: Knowing the degree of semantic contrast between words has widespread application in natural language processing, including machine translation, information retrieval, and dialogue systems. Manually-created lexicons focus on opposites, such as {\rm hot} and {\rm cold}. Opposites are of many kinds such as antipodals, complementaries, and gradable. However, existing lexicons often do not classify opposites into the different kinds. They also do not explicitly list word pairs that are not opposites but yet have some degree of contrast in meaning, such as {\rm warm} and {\rm cold} or {\rm tropical} and {\rm freezing}. We propose an automatic method to identify contrasting word pairs that is based on the hypothesis that if a pair of words, $A$ and $B$, are contrasting, then there is a pair of opposites, $C$ and $D$, such that $A$ and $C$ are strongly related and $B$ and $D$ are strongly related. (For example, there exists the pair of opposites {\rm hot} and {\rm cold} such that {\rm tropical} is related to {\rm ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1308.6300 https://dx.doi.org/10.48550/arxiv.1308.6300 BASE Hide details

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